Bioinformatics Analyst III

FULLY REMOTEPay: $60-$65/hrREQUIRED:Candidate Profile & Must-HavesMaster’s with strong experience or PhD/fresh PhD acceptableMust be very hands-on technically — this role involves heavy coding day-to-dayStrong proficiency in both R and Python requiredMust have experience working in HPC or cloud environments for parallel computingMust have experience processing and analyzing omics data (any type acceptable):RNA-seqscRNA-seqCRISPR screensproteomicsimagingother multi-omics datasetsAbility to extract biological insight from omics data and translate scientific questions into computational analysesStrong statistics foundation including regression, modeling concepts, interpretation, and visualizationFamiliarity with AI/ML concepts and LLM-assisted workflows/tools (ChatGPT, Claude, etc.)What the Manager Seems to ValueStrong coding ability over purely theoretical knowledgeCandidates who can independently work with datasets, propose analyses, and troubleshoot workflowsPractical computational biology/bioinformatics experience with real datasetsStrong documentation and communication skills:clean reportingreproducible workflowsGitHub/README-style organizationPharma or translational research experience is helpful, but strong academic backgrounds are still viableNice-to-HavesCRISPR perturbation analysisDrug target ranking/prioritization workSpatial or multiplexed omicsAI/ML model development experienceData visualization for scientific/non-technical audiencesExperience with target valuation or indication strategy work in oncology or related disease areasThe successful candidate will work closely with stakeholders in the Quantitative Insights Lab (QuIL) organization to support cross-project data science efforts spanning in silico perturbation analysis, toxicogenomics, and mechanistic profiling. This work will contribute to the development and evaluation of predictive and comparative frameworks that help rank drug targets, drug combinations, and biological signatures across multiple experimental systems. This will include integrating large-scale omics data to support combination strategy assessment across indications.Key Responsibilities• Support perturbation analyses to rank promising drug targets and drug combinations for efficacy prediction.• Contribute to the development of combination signature estimation approaches and ranking frameworks.• Ingest, clean, and preprocess multi-modal datasets to enable downstream analysis and modeling.• Apply AI/ML methods where appropriate, including the use of large language models to accelerate analysis, coding, and workflow development.• Demonstrate understanding of model development principles, including training, testing, and cross-validation, and help select appropriate models for specific problem types.• Collaborate with scientific and technical teams to translate biological questions into computational solutions, execute and troubleshoot analyses, and communicate findings clearly and reproducibly.Qualifications• MS degree with 5+ years of experience or PhD with 0+ years of experience in a quantitative field such as Bioinformatics, Computer Science, Computational Genetics, Biostatistics, AI/ML, or a related discipline with strong computational training.• Proficiency in R or Python and standard statistics/ML libraries.• Experience working in HPC or cloud environments for parallel computing• Domain knowledge in bioinformatics, computational biology, or related omics-driven data science.• Strong attention to detail, documentation, and communication skills.• Ability to independently execute ideas and research solutions within project scope.Required Technical Skills1. Experience processing and analyzing RNA-seq, imaging data, CRISPR screens, or other similar NGS/genomic data.2. Proficient in statistical and programming languages such as R and Python, with ability to perform parallel computing using HPC3. Experience writing custom functions in R or Python to statistically interrogate and visualize omics data.4. Demonstrated ability to execute custom computational analysis plans leveraging Client algorithms, relevant databases, and AI/ML approaches where appropriate.5. Familiarity with machine learning fundamentals, including model selection, training, testing, and cross-validation.6. Experience using LLM-based tools or coding assistants to support analysis, coding, and workflow development.